Tolvaptan in Autosomal Dominant Polycystic Kidney Disease (ADPKD): a multicenter real life Italian experience
Bibliographic record
Abstract
Background: Tolvaptan is the only disease-modifying treatment approved to slow kidney disease progression in autosomal dominant polycystic kidney disease (ADPKD). Data on tolvaptan in ADPKD outside of clinical trials are limited, usually deal with short-term observations and are characterized by a drop-out rate of 21%-56%. Methods: In this retrospective, observational, multicenter study enrolling 122 Italian patients with ADPKD and rapid progressive renal disease, we evaluated the tolerability and side effects of tolvaptan treatment in a real-life setting. A personalized treatment strategy, based on an accurate patient selection and a strict clinical and laboratory monitoring, was applied. Results: During a median follow-up of 34.0 (interquartile range 17.3; -46.5) months the overall adherence on tolvaptan was 82.0%. Permanent withdrawal was reported in 22/122 patients (18.0%) after a mean of 34.4 ± 18.0 months; the main reasons for drug permanent withdrawal were onset of kidney failure in 7/122 (5.7%), poor tolerance to aquaresis in 4/122 (3.3%), family planning in 3/122 (2.5%) and liver function test elevation in 2/122 patients (1.6%). Temporary discontinuation was observed in 35/122 patients (28.7%) and in most cases it was not related to drug side effects. In order to evaluate tolvaptan eligibility, historic estimated glomerular filtration rate decline was the most inclusive criteria (92.9%) when compared with Mayo Imaging Classification (89.7%) and Predicting Renal Outcome in Polycystic Kidney Disease (PROPKD) Score (22.6%). Conclusion: This real-life study confirms the feasibility, safety, and tolerability of tolvaptan treatment. In order to overcome the challenges of tolvaptan treatment, our experience suggests that a dedicated PKD team may play a key role in implementing strategies focused to reduce drop-out and achieve treatment success.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".